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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #341516

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Evaluate the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

item HERMAN, MATHEW - Michigan State University
item NAJADHASHEMI, POUYAN A. - Michigan State University
item ABOUALI, MOHAMMAD - Michigan State University
item HERNANDEZ-SUAREZ, JUAN SEBASTIAN - Michigan State University
item DANESHVAR, FARIBORZ - Michigan State University
item ZHEN, ZHANG - University Of Chicago
item Anderson, Martha
item Sadeghi, Ali
item HAIN, C. - Goddard Space Flight Center
item SHARIFI, AMIR - University Of Maryland

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/6/2017
Publication Date: 11/9/2017
Citation: Herman, M., Najadhashemi, P., Abouali, M., Hernandez-Suarez, J., Daneshvar, F., Zhen, Z., Anderson, M.C., Sadeghi, A.M., Hain, C., Sharifi, A. 2017. Evaluate the role of evapotranspiration remote sensing data in improving hydrological modeling predictability. Journal of Hydrology. 556:39-49.

Interpretive Summary: An expected rise in the world’s population will demand increased use of freshwater resources. To ensure the sustainability of regional freshwater resources, a hybrid system of monitoring and hydrological modeling will often be employed. Monitoring, however, is only feasible for small scale assessments where representative ground-based measurements are available. Hydrologic models are commonly used for larger scales, such as watersheds and regional evaluations, but their use can also be challenging. For example, most hydrological models are routinely calibrated for only one component of the hydrological cycle, such as streamflow. In this study, our goal was to examine whether the addition of another hydrological cycle component such as actual evapotranspiration (ETa) to the model calibration process can improve or reduce the overall model calibration accuracy. To examine this question, we used two different calibration techniques – a multi-variable and genetic algorithm approach - along with two different sets of observed satellite-based remote sensing ETa data estimations: one from the USGS and the other, based on the Atmosphere-Land Exchange Inverse (ALEXI) model. Overall, the hydrological models developed from both calibration techniques were successful at improving the ETa simulations. However, the use of the genetic algorithm technique, while able to more accurately calibrate ET values, significantly reduced the streamflow calibration accuracy. Meanwhile, the multi-variable technique performed well and was able to maintain a high level of acceptance for the streamflow calibration and also improved the ET estimations, suggesting that this approach produces better agreement when compared to the genetic algorithm approach.

Technical Abstract: As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values > 0.5 (0.73 to 0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error -observations standard deviation ratio (RSR) values < 0.7 (0.39 to 0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32 to 0.52, PBIAS: ±32.73%, and RSR: 0.63 to 0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59 to 0.61, PBIAS: ±13.70%, and RSR: 0.63 to 0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.